Towards a Personalized Prescription Tool for Diabetic Treatment.

摘要

For Diabetic patients, insulin is unable to effectively assist in transporting glucose into cells to be used for energy. Type I diabetes arises when the pancreas does not produce enough insulin, and type II diabetes develops when cell receptors become insensitive to insulin. Both conditions present the danger of causing unhealthy glucose levels in the blood stream and are treated with insulin injection therapy to trigger glucose uptake in the cells. A mathematical model of natural glucose and insulin control allows for a quantitative understanding of the internal glucose-insulin dynamics of healthy and diabetic patients. This research extends the Cobelli model of glucose and insulin dynamics to include both long and short-acting insulin inputs currently used to treat diabetic patients. The project will introduce a personalized approach to treatment by adapting the set of average diabetic Cobelli model parameters over time in response to observed patient feedback data. The personalized model will be combined with a nonlinear model predictive control strategy to determine the best insulin injection routine to achieve healthy glucose levels in diabetic patients. This work will contribute to the development of an individualized prescription tool which physicians can use to more effectively treat diabetic patients.